Heilmaier, M.; Krüger, M.; Pyczak, F.; Schloffer, M.; Stein, F. (Eds.): Intermetallics 2023. Intermetallics 2023, Bad Staffelstein, Germany, October 02, 2023 - October 06, 2023. Conventus Congressmanagement & Marketing GmbH, Jena, Germany (2023), 122 pp.
Heilmaier, M.; Krüger, M.; Palm, M.; Pyczak, F.; Stein, F. (Eds.): Intermetallics 2021. Intermetallics 2021, Kloster Banz, Bad Staffelstein, Germany, October 04, 2021 - October 08, 2021. Conventus Congressmanagement & Marketing GmbH, Jena, Germany (2021), 208 pp.
Heilmaier, M.; Krüger, M.; Mayer, S.; Palm, M.; Stein, F. (Eds.): Proceedings Intermetallics 2019. Intermetallics 2019, Educational Center Kloster Banz, Bad Staffelstein, Germany, September 30, 2019 - October 04, 2019. Conventus Congressmanagement & Marketing GmbH, Jena, Germany (2019)
Heilmaier, M.; Krüger, M.; Mayer, S.; Palm, M.; Stein, F. (Eds.): Proceedings Intermetallics 2017. Intermetallics 2017, Educational Center Kloster Banz, Bad Staffelstein, Germany, October 02, 2017 - October 06, 2017. Congressmanagement & Marketing GmbH, Jena, Germany (2017), 220 pp.
Heilmaier, M.; Krüger, M.; Mayer, S.; Palm, M.; Stein, F. (Eds.): Proceedings: Intermetallics 2015, International Conference. Intermetallics 2015, International Conference, Bad Staffelstein, Germany, September 28, 2015 - October 02, 2015. Congressmanagement & Marketing GmbH, Jena, Germany (2015), 116 pp.
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…